Support vector machines are used in binary classification of samples. A support vector machine is typically constructed using a set of training samples and quadratic programming techniques to solve the difficult computational problem of identifying a suitable classification hyperplane in the relevant data space. With multi-dimensional space, or a large number of samples, the building of a support vector machine using indirect quadratic programming is complex and difficult and often is unable to adapt to changes in the set of training samples.
Similar reference numerals may have been used in different figures to denote similar components.